Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.

Journal: Applied clinical informatics
Published Date: May 25, 2022

Abstract

OBJECTIVE: As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations.

Authors

  • Lane Fitzsimmons
    College of Agriculture and Life Science, Cornell University, Ithaca, New York, United States.
  • Maya Dewan
    Department of Pediatrics, University of Cincinnati College of Medicine (M Dewan, M Klein, and M Zackoff), Cincinnati, Ohio; Division of Critical Care Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center (C Merritt, M Dewan, and M Zackoff), Cincinnati, Ohio.
  • Judith W Dexheimer
    Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA Division of Pediatric Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

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